Leveraging personal metadata for Desktop search: The Beagle++ system

Enrico Minack, Raluca Paiu, Stefania Costache, Gianluca Demartini, Julien Gaugaz, Ekaterini Ioannou, Paul-Alexandru Chirita

Abstract


Search on PCs has become less efficient than searching the Web due to the increasing amount of stored
data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata,
context information as well as additional background information for improving Desktop search results.
We also present a practical application of this approach—the extensible Beagle++ toolbox. To prove the
validity of our approach, we conducted a series of experiments. By comparing our results against the
ones of a regular Desktop search solution – Beagle – we show an improved quality in search and overall
performance

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Type of Paper: research paper
Keywords: Semantic Desktop search; Personal information management; Automatic metadata generation
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